Embodiments of the present invention relate to video test and measurement, and more particularly to video picture quality (PQ) measurements.
For picture quality analysis, classification of artifacts has long been acknowledged as important and is incorporated in present international standards for methods of picture quality measurement, such as ITU-T J.144. The method presented here allows for analysis of proportions of each class of artifact detected as a contribution to the total impairment measurement, whether objective, as in PSNR, or subjective, as in predicted DMOS. Such dissection of impairments by class is valuable for diagnostic analysis of video processing components, whether components within a video compression encoder or decoder, or a component in a video broadcast chain.
Video compression methods such as MPEG-2 and H.264 process video use lossy compression methods which introduce errors, ideally unseen by the human eye. Any visible error caused by loss in the compression method manifests itself as an impairment artifact which may, or may not, effect the perceived quality of the video. There are different types of impairments, each with different levels of objectionability. Thus, identification of artifacts and weighting the magnitude of the artifact by it's objectionability has been a popular approach to predicting subjective video quality ratings, see ITU-T J.144.
Examples of artifacts seen from video compression are contouring in otherwise smooth gradients, staircase noise along curving edges, “mosquito noise” or ringing around edges, and/or checkerboarding in “busy” regions (sometimes called quilting or blockiness) and blurring. In addition to these compression artifacts, noise or less image correlated artifacts can occur, appearing more like errors of individual pixels as in “snowy” or “speckled” images from a weak analog reception.
Various approaches have been proposed to reduce the effects of image compression. Often artifact reduction algorithms may introduce artifacts of their own, having a method of checking relative proportions of artifacts is useful for video processing HW and SW developers.
Prior art has required mostly separate processing for identification of each artifact, and computationally expensive approaches, in terms of computation time and the cost to provide the necessary level of computing power, which also generally does not guarantee optimal orthogonality (or separation) and accurate relative measure among artifact classes detected. In addition, prior art lacks “completeness” in the sense that the sum of artifacts classified do not necessarily include all differences between reference and test video.